{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import h5py\n",
    "import scipy.io\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "matFilename = './Data/2017-05-12_batchdata_updated_struct_errorcorrect.mat'\n",
    "f = h5py.File(matFilename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#refs#', '#subsystem#', 'batch', 'batch_date']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(f.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "batch = f['batch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Vdlin',\n",
       " 'barcode',\n",
       " 'channel_id',\n",
       " 'cycle_life',\n",
       " 'cycles',\n",
       " 'policy',\n",
       " 'policy_readable',\n",
       " 'summary']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(batch.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "num_cells = batch['summary'].shape[0]\n",
    "bat_dict = {}\n",
    "for i in range(num_cells):\n",
    "    cl = f[batch['cycle_life'][i,0]].value\n",
    "    policy = f[batch['policy_readable'][i,0]].value.tobytes()[::2].decode()\n",
    "    summary_IR = np.hstack(f[batch['summary'][i,0]]['IR'][0,:].tolist())\n",
    "    summary_QC = np.hstack(f[batch['summary'][i,0]]['QCharge'][0,:].tolist())\n",
    "    summary_QD = np.hstack(f[batch['summary'][i,0]]['QDischarge'][0,:].tolist())\n",
    "    summary_TA = np.hstack(f[batch['summary'][i,0]]['Tavg'][0,:].tolist())\n",
    "    summary_TM = np.hstack(f[batch['summary'][i,0]]['Tmin'][0,:].tolist())\n",
    "    summary_TX = np.hstack(f[batch['summary'][i,0]]['Tmax'][0,:].tolist())\n",
    "    summary_CT = np.hstack(f[batch['summary'][i,0]]['chargetime'][0,:].tolist())\n",
    "    summary_CY = np.hstack(f[batch['summary'][i,0]]['cycle'][0,:].tolist())\n",
    "    summary = {'IR': summary_IR, 'QC': summary_QC, 'QD': summary_QD, 'Tavg':\n",
    "                summary_TA, 'Tmin': summary_TM, 'Tmax': summary_TX, 'chargetime': summary_CT,\n",
    "                'cycle': summary_CY}\n",
    "    cycles = f[batch['cycles'][i,0]]\n",
    "    cycle_dict = {}\n",
    "    for j in range(cycles['I'].shape[0]):\n",
    "        I = np.hstack((f[cycles['I'][j,0]].value))\n",
    "        Qc = np.hstack((f[cycles['Qc'][j,0]].value))\n",
    "        Qd = np.hstack((f[cycles['Qd'][j,0]].value))\n",
    "        Qdlin = np.hstack((f[cycles['Qdlin'][j,0]].value))\n",
    "        T = np.hstack((f[cycles['T'][j,0]].value))\n",
    "        Tdlin = np.hstack((f[cycles['Tdlin'][j,0]].value))\n",
    "        V = np.hstack((f[cycles['V'][j,0]].value))\n",
    "        dQdV = np.hstack((f[cycles['discharge_dQdV'][j,0]].value))\n",
    "        t = np.hstack((f[cycles['t'][j,0]].value))\n",
    "        cd = {'I': I, 'Qc': Qc, 'Qd': Qd, 'Qdlin': Qdlin, 'T': T, 'Tdlin': Tdlin, 'V':V, 'dQdV': dQdV, 't':t}\n",
    "        cycle_dict[str(j)] = cd\n",
    "        \n",
    "    cell_dict = {'cycle_life': cl, 'charge_policy':policy, 'summary': summary, 'cycles': cycle_dict}\n",
    "    key = 'b1c' + str(i)\n",
    "    bat_dict[key]=   cell_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['b1c0', 'b1c1', 'b1c2', 'b1c3', 'b1c4', 'b1c5', 'b1c6', 'b1c7', 'b1c8', 'b1c9', 'b1c10', 'b1c11', 'b1c12', 'b1c13', 'b1c14', 'b1c15', 'b1c16', 'b1c17', 'b1c18', 'b1c19', 'b1c20', 'b1c21', 'b1c22', 'b1c23', 'b1c24', 'b1c25', 'b1c26', 'b1c27', 'b1c28', 'b1c29', 'b1c30', 'b1c31', 'b1c32', 'b1c33', 'b1c34', 'b1c35', 'b1c36', 'b1c37', 'b1c38', 'b1c39', 'b1c40', 'b1c41', 'b1c42', 'b1c43', 'b1c44', 'b1c45'])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bat_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271b15548d0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bat_dict['b1c43']['summary']['cycle'], bat_dict['b1c43']['summary']['QD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271879be0b8>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bat_dict['b1c43']['cycles']['10']['Qd'], bat_dict['b1c43']['cycles']['10']['V'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('batch1.pkl','wb') as fp:\n",
    "        pickle.dump(bat_dict,fp)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py_36_env",
   "language": "python",
   "name": "py_36_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
